The diffusion of NLP methods in marketing research

A systematic analysis

Olivier Caron
olivier.caron@dauphine.psl.eu

Paris Dauphine - PSL

Christophe Benavent
christophe.benavent@dauphine.psl.eu

Paris Dauphine - PSL

May 6, 2024

Research context

NLP methods are increasingly used in marketing research

NLP methods enable the conversion of text into quantifiable data for in-depth analysis enabling the analysis of large volumes of text.

They are particularly suitable for marketing concerns:

  • Evaluate customer feebacks, gauging public sentiment
  • Detect emotional responses to products and marketing campaigns
  • Detect trends, consumer preferences and needs.

Specific research practices

Institutional Framework promote competition in academia driven by journal rankings and citation scores, influencing researcher compensation and career advancement. (Richard et al., 2015)

Concentration of research in major publishers (Elsevier, Thomson) with an increasing number of journals with facilitated access through bibliographic search interfaces.

Research strategies

Researcher must elaborate strategies to advance their career (Kolesnikov et al., 2018)

  • Balance between productivity, impact, originality, legitimacy, learning costs.

These strategies can be thought of in terms of the technology acceptance model from Davis (1989)

  • Adopt NLP methods based on interest in innovative tools (1) and the perceived ease of integrating these methods into their work (2)

Conceptual framework

Data presentation

Articles

  • There are 682 articles and 2310 different authors

  • Date of publication range from 1985 to 2023

Data collection

  • All data were collected from Scopus.

Keywords

  • “natural language processing”, “nlp”, “embeddings”, “chatgpt”, “liwc”, “transformers”, “word2vec”, “wordtovec”, “lda”, “text mining”, “text-mining”, “text analysis”, “text analytics”, “text-analytics”, “text-analysis”

Journals

  • 156 journals ranked from the first to third quartile according to the SCImago Journal Rank in the field of marketing.

General data vizualisation

Production per affiliation

Management trend in publication volume

Networks & Communities

The structure of author networks (until 2015)

The structure of author networks (until 2023)

Some measures of network structure

The unequal spread of citations between communities

The top 5 communities account for 13.65% of authors (126) and 46% of citations (14851)

Topics

Topic modeling with STM

A concentric diffusion of topics

A focus on methods

Main NLP techniques used

  • “O” or “1” if a technique is detected in the articles

  • combined text of abstract, title, and keywords

  • detection of the technique name and the different spellings

bert_alt <- c(
  "bert",
  "bi-directional encoder representation from transformers",
  "bi-directional encoder representations from transformer",
  "bi-directional encoder representations from transformers",
  "bidirectional encoder representation from transformer",
  "bidirectional encoder representation from transformers",
  "bidirectional encoder representations from transformer",
  "bidirectional encoder representations from transformers",
  "bi directional encoder representation from transformer",
  "bi directional encoder representation from transformers",
  "bi directional encoder representations from transformer",
  "bi directional encoder representations from transformers"
)

Evolution of NLP techniques

Sentiment_Analysis Topic_Modeling LDA LIWC Neural_networks ChatGPT Embeddings Leximancer BERT STM Transformers Word2Vec LLM RoBERTa GPT2 PARA PassivePy NER GloVe T5 TFIDF Camembert Flaubert FastText TextRank GPT3 POS_Tagging
81 68 53 26 26 20 19 13 8 6 6 6 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0

Diffusion and delay of NLP techniques

Dominantly marketing: the selective borrowing of NLP methods

Future of NLP in marketing

  • NLP methods’ adoption in top-tier marketing journals suggests it’s not a mere trend but an innovation gaining solid ground in the field.
  • LLMs lower learning costs for sentiment analysis and classification tasks
  • LLMs enable agent based simulation (Törnberg et al., 2023)
  • Fine-tuned machine learning methods still perform better for specific tasks (Krugmann & Hartmann, 2024)
  • but LLMs achieve very good results without prior training

References

Abrahamson, E. (1991). Managerial fads and fashions: The diffusion and rejection of innovations. The Academy of Management Review, 16(3), 586. https://doi.org/10.2307/258919
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Hauser, J., Tellis, G. J., & Griffin, A. (2006). Research on Innovation: A Review and Agenda for "Marketing Science". Marketing Science, 25(6,), 687–717. http://www.jstor.org/stable/40057216
Kolesnikov, S., Fukumoto, E., & Bozeman, B. (2018). Researchers’ risk-smoothing publication strategies: Is productivity the enemy of impact? Scientometrics, 116(3), 1995–2017. https://doi.org/10.1007/s11192-018-2793-8
Krugmann, J. O., & Hartmann, J. (2024). Sentiment Analysis in the Age of Generative AI. Customer Needs and Solutions, 11(1), 3. https://doi.org/10.1007/s40547-024-00143-4
Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation (1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9780511815355
Richard, J., Plimmer, G., Fam, K.-S., & Campbell, C. (2015). Publishing success of marketing academics: antecedents and outcomes. European Journal of Marketing, 49(1/2), 123–145. https://doi.org/10.1108/EJM-06-2013-0311
Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., & Rand, D. G. (2014). Structural Topic Models for Open-Ended Survey Responses. American Journal of Political Science, 58(4), 1064–1082. https://doi.org/10.1111/ajps.12103
Törnberg, P., Valeeva, D., Uitermark, J., & Bail, C. (2023). Simulating social media using large language models to evaluate alternative news feed algorithms. arXiv Preprint arXiv:2310.05984.

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